Deep Learning with Python - Optimizing Deep Learning Models

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 01 - Introduction
• 01 - Optimizing deep learning models.mp4 (3.5 MB)
• 01 - Optimizing deep learning models.srt (1.5 KB)
• 02 - What you should know.mp4 (1.2 MB)
• 02 - What you should know.srt (1.4 KB)
• 03 - Using the exercise files.mp4 (2.8 MB)
• 03 - Using the exercise files.srt (2.1 KB)
02 - 1. Optimizing Deep Learning Models
• 01 - The importance of optimizing deep learning models.mp4 (6.0 MB)
• 01 - The importance of optimizing deep learning models.srt (5.1 KB)
03 - 2. Regularization Techniques
• 01 - The bias-variance trade-off.mp4 (5.9 MB)
• 01 - The bias-variance trade-off.srt (5.7 KB)
• 02 - Lasso and ridge regularization.mp4 (7.5 MB)
• 02 - Lasso and ridge regularization.srt (6.5 KB)
• 03 - Applying L1 regularization to a deep learning model.mp4 (9.8 MB)
• 03 - Applying L1 regularization to a deep learning model.srt (4.6 KB)
• 04 - Applying L2 regularization to a deep learning model.mp4 (9.6 MB)
• 04 - Applying L2 regularization to a deep learning model.srt (4.9 KB)
• 05 - Elastic Net regularization.mp4 (4.7 MB)
• 05 - Elastic Net regularization.srt (4.0 KB)
• 06 - Dropout regularization.mp4 (4.7 MB)
• 06 - Dropout regularization.srt (4.5 KB)
• 07 - Applying dropout regularization to a deep learning model.mp4 (9.9 MB)
• 07 - Applying dropout regularization to a deep learning model.srt (4.4 KB)
04 - 3. Loss Functions and Optimization Algorithms
• 01 - Common loss functions in deep learning.mp4 (10.6 MB)
• 01 - Common loss functions in deep learning.srt (7.9 KB)
• 02 - Batch gradient descent.mp4 (5.6 MB)
• 02 - Batch gradient descent.srt (5.9 KB)
• 03 - Stochastic gradient descent (SGD).mp4 (4.6 MB)
• 03 - Stochastic gradient descent (SGD).srt (4.8 KB)
• 04 - Mini-batch gradient descent.mp4 (5.5 MB)
• 04 - Mini-batch gradient descent.srt (6.1 KB)
• 05 - Adaptive Gradient Algorithm (AdaGrad).mp4 (8.7 MB)
• 05 - Adaptive Gradient Algorithm (AdaGrad).srt (7.6 KB)
• 06 - Root Mean Square Propagation (RMSProp).mp4 (4.3 MB)
• 06 - Root Mean Square Propagation (RMSProp).srt (3.8 KB)
• 07 - Adaptive Delta (AdaDelta).mp4 (2.6 MB)
• 07 - Adaptive Delta (AdaDelta).srt (2.8 KB)
• 08 - Adaptive Moment Estimation (Adam).mp4 (6.1 MB)
• 08 - Adaptive Moment Estimation (Adam).srt (5.2 KB)
05 - 4. Hyperparameter Tuning Techniques
• 01 - Parameters versus hyperparameters.mp4 (9.7 MB)
• 01 - Parameters versus hyperparameters.srt (7.4 KB)
• 02 - Key hyperparameters in deep learning.mp4 (14.9 MB)
• 02 - Key hyperparameters in deep learning.srt (12.6 KB)
• 03 - Methods for hyperparameter tuning.mp4 (10.3 MB)
• 03 - Methods for hyperparameter tuning.srt (10.2 KB)
• 04 - Defining a tunable deep learning model in Keras.mp4 (17.9 MB)
• 04 - Defining a tunable deep learning model in Keras.srt (9.7 KB)
• 05 - Using KerasTuner for hyperparameter tuning.mp4 (23.5 MB)
• 05 - Using KerasTuner for hyperparameter tuning.srt (11.5 KB)
06 - 5. Advanced Training Techniques
• 01 - Batch normalization.mp4 (5.0 MB)
• 01 - Batch normalization.srt (4.9 KB)
• 02 - Applying batch normalization to a deep learning model.mp4 (8.0 MB)
• 02 - Applying batch normalization to a deep learning model.srt (4.4 KB)
• 03 - Gradient clipping.mp4 (9.4 MB)
• 03 - Gradient clipping.srt (7.8 KB)
• 04 - Applying gradient clipping to a deep learning model.mp4 (9.0 MB)
• 04 - Applying gradient clipping to a deep learning model.srt (4.4 KB)
• 05 - Early stopping and checkpointing.mp4 (5.1 MB)
• 05 - Early stopping and checkpointing.srt (5.6 KB)
• 06 - Learning rate scheduling.mp4 (11.1 MB)
• 06 - Learning rate scheduling.srt (8.2 KB)
• 07 - Training a deep learning model using callbacks.mp4 (20.0 MB)
• 07 - Training a deep learning model using callbacks.srt (10.2 KB)
07 - Conclusion
• 01 - Continuing to optimize deep learning models.mp4 (6.1 MB)
• 01 - Continuing to optimize deep learning models.srt (5.4 KB)
• Bonus Resources.txt (0.1 KB)